Film to dicom conversion

ABSTRACT

Embodiments digitize radiology films into DICOM format. Radiology films typically include an array of captured images laid out in a grid pattern. To comply with DICOM format, the scanned image of a radiology film is segmented into sub-images and text is extracted from the sub-images to generate DICOM metadata. The sub-images and extracted text metadata are then combined to generate a DICOM-compliant multi-image file.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/199,086, filed Mar. 6, 2014, to be issued as U.S. Pat. No. 9,396,306on Jul. 19, 2016, the content of which is incorporated herein byreference in its entirety.

BACKGROUND Field of Disclosure

This disclosure relates generally to the conversion of radiological filmto DICOM-compliant image files.

Description of the Related Art

Physical radiology films document medical images that are capturedduring medical scans conducted on patients. These images aresubsequently used by doctors, researchers, and others to diagnoseconditions and develop treatments. Although consulting the physicalradiology films is still common in some parts of the world such asIndia, the size and formatting of these physical radiology films lead todifficulties in transferring them to others and archiving them forstorage.

In part to respond to the increased use of computers in clinicalapplications, the American College of Radiology and the NationalElectrical Manufacturers Association developed a standard method fortransferring images and associated information between devicesmanufactured by various vendors. These entities developed the DigitalImaging and Communications in Medicine (DICOM) standard. DICOM-compliantfiles can be transferred between computing systems in a multi-vendorenvironment.

SUMMARY

Embodiments of the invention digitize radiology films into DICOM format.Radiology films typically include an array of captured images laid outin a grid pattern. To comply with DICOM format, the scanned image of aradiology film is segmented into sub-images and text is extracted fromthe sub-images to generate DICOM metadata. The sub-images and extractedtext metadata are then combined to generate a DICOM-compliantmulti-image file.

Embodiments of the invention include methods of processing radiologyfilm images into individual sub-images and metadata that are thencombined to generate a DICOM compliant multi-image file. Embodiments ofthe computer-readable storage medium store computer-executableinstructions for performing the steps described above. Embodiments ofthe system further comprise a processor for executing thecomputer-executable instructions.

The features and advantages described in the specification are not allinclusive and, in particular, many additional features and advantageswill be apparent to one of ordinary skill in the art in view of thedrawings, specification, and claims. Moreover, it should be noted thatthe language used in the specification has been principally selected forreadability and instructional purposes, and may not have been selectedto delineate or circumscribe the inventive subject matter.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a high-level block diagram illustrating an embodiment of anenvironment for digitizing radiology film into DICOM format.

FIG. 2 is a block diagram illustrating an example computer forimplementing the converter system shown in FIG. 1.

FIG. 3 is a block diagram illustrating an image segmentation module of aconverter system in accordance with an embodiment.

FIG. 4 is a flowchart illustrating a method of performing grid blocksegmentation on an image of a radiology film in accordance with anembodiment.

FIG. 5 is a flowchart illustrating a method of processing an image todetect straight lines and remove false-positive gridlines in accordancewith an embodiment.

FIG. 6 is a flowchart illustrating a method of identifying a gridpattern in an image of a radiology film.

FIG. 7A illustrates an example graph of the length of horizontalHoughLines at various distances from the origin. FIG. 7B illustrates anexample output of lines from the statistical analysis of the data inFIG. 7A according to the method illustrated in FIG. 6.

FIG. 8A illustrates an example graph of the length of verticalHoughLines at various distances from the origin. FIG. 8B illustrates anexample output of lines from the statistical analysis of the data inFIG. 8A according to the method illustrated in FIG. 6.

FIG. 9 is a block diagram illustrating a metadata extraction module of aconverter system in accordance with an embodiment.

FIG. 10 is a flowchart illustrating a method of text region detection inaccordance with an embodiment.

FIG. 11 is a flowchart illustrating a method of processing text regionsto prepare them for text recognition in accordance with an embodiment.

FIG. 12 illustrates a comparison between text regions from the originalimage, text regions processed according to a conventional OtsuThresholding technique, and text regions processed according totechniques in accordance with an embodiment of the invention, referredto as MinCE with hole filing.

FIG. 13 is a flowchart illustrating a method of performing textrecognition in accordance with an embodiment.

FIG. 14 is a flowchart illustrating a method of generating a DICOM filein accordance with an embodiment.

The Figures (FIGS.) and the following description describe certainembodiments by way of illustration only. One skilled in the art willreadily recognize from the following description that alternativeembodiments of the structures and methods illustrated herein may beemployed without departing from the principles described herein.

DETAILED DESCRIPTION

Embodiments of the invention digitize radiology films into DICOM format.Radiology films typically include an array of captured images laid outin a grid pattern. To comply with DICOM format, the scanned image of aradiology film is segmented into sub-images and text is extracted fromthe sub-images to generate DICOM metadata. The sub-images and extractedtext metadata are then combined to generate a DICOM-compliantmulti-image file.

In one embodiment, the radiology film is scanned using a conventionalfilm digitizer. The scan of the radiology film results in one imagecomposed of several sub-images in a grid pattern. By accuratelyidentifying the gridlines between the sub-images, the system can segmentthe image into sub-image. The gridlines are identified by using anedge-detection pre-processing step followed by a Hough Transform todetect straight lines in the image. Then, by using a statisticalanalysis that separately considers the length of the detected lines atvertical positions and at horizontal positions, false positives can beremoved from the group of detected straight lines. The remainingstraight lines are clustered together if they are within a shortdistance of each other. The mean position of each cluster of lines inthe vertical direction is then identified as the center of a verticalgridline, and the mean position of each cluster of lines in thehorizontal direction is then identified as the center of a horizontalgridline. The identified vertical and horizontal gridlines are then usedto segment the image into sub-images.

In one embodiment, each segmented sub-image is analyzed to detect textregions. The horizontal and vertical edges present in text regions arefound by using a Sobel operator along the X direction and using a Sobeloperator along the Y direction. Then, dilation along the X direction anddilation along the Y direction are performed to further process thesub-images. The resulting two versions of the sub-image are thencombined using a binary AND operation. Additionally, connected componentanalysis is performed to identify the boundaries of the regions of thesub-image where text is present.

Once regions where text is present are identified, the text regions areprocessed to enhance the readability of the text before it is read by anOptical Character Recognition (OCR) engine. Each image region identifiedas containing text is sharpened, a power law transformation isperformed, and then Minimum Cross Entropy (to select the best thresholdto avoid unnecessary losses of text information during the processing)and hole filing techniques are used on the regions before bitmanipulations such as an XOR operation is performed. The resultingprocessed text region is then prepared for an OCR engine to extract textfrom the region.

In one embodiment, the result of the text extraction can be evaluatedusing the Levenshtein Distance between the ground truth (the actualtext) and the OCR text. For example, the analysis can be performed using1-e/c, where e is the Levenshtein distance, and c is the number ofcharacters. The extracted text from the OCR engine can be formatted intoDICOM metadata to accompany the sub-image from which it was extracted.

Thus, embodiments of the invention include methods of processingradiology film images into individual sub-images and metadata that arethen combined to generate a DICOM compliant multi-image file.Embodiments of the computer-readable storage medium storecomputer-executable instructions for performing the steps describedabove. Embodiments of the system further comprise a processor forexecuting the computer-executable instructions.

System Overview

FIG. 1 is a high-level block diagram illustrating an embodiment of anenvironment for digitizing radiology film into DICOM format. Theenvironment includes a radiology film 101, a converter system 100, and aDICOM-compliant file 141.

The radiology film 101 is a physical copy of the images obtained, forexample, during a medical scan. The images typically contain largeintensity variations due to the presence of bone, soft tissue, and otheranatomical structures in the images. The film 101 is typically laid outin a grid pattern comprising sub-images and embedded text thatidentifies the patient, the date, the measurement scale, and otherinformation that may be used as DICOM metadata. The boundaries betweenthe sub-images in the grid pattern are referred to herein as gridlines.The grid and the gridlines may be rotationally skewed with respect tothe straight edges of the film 101.

The converter system 100 converts an input radiology film 101 into aDICOM-compliant file 141. The converter system scans the radiology film101, segments the resulting image into individual sub-images, extractsDICOM metadata from the scanned image, and combines the sub-images withthe extracted metadata to generate a DICOM-compliant file 141. Theconverter system 100 includes a film digitizer 110, an imagesegmentation module 120, a metadata extraction module 130, and a DICOMfile generation module 140.

The film digitizer 110 scans the input radiology film 101 to create adigital image. Any conventional film scanner may be used as filmdigitizer 110, such as a scanner with resolution of 300 dpi.

The image segmentation module 120 segments the digital image created bythe film digitizer 110 into individual sub-images. The imagesegmentation module 120 identifies straight lines in the image that formthe gridlines between sub-images. The operation of the imagesegmentation module is described in detail below with reference to FIGS.3-8.

The metadata extraction module 130 extracts DICOM metadata from theindividual sub-images identified by the image segmentation module 120.The metadata extraction module 130 detects text regions within thesub-image, processes the text regions to enhance the clarity of thetext, and performs text recognition on the text of the regions. Theoperation of the metadata extraction module 130 is described in detailbelow with reference to FIGS. 9-13.

The DICOM file generation module 140 combines the individual sub-imageswith their respective metadata, including text from text regions withinthe sub-images to generate a DICOM-compliant file 141. TheDICOM-compliant file 141 is typically a multi-image file with metadatathat provides patient information and information about the image in astandardized format, for example National Electrical ManufacturersAssociation (NEMA) standard PS3, 2011 version. The operation of theDICOM file generation module 140 is described in detail below withreference to FIG. 14.

FIG. 2 is a block diagram illustrating an example computer forimplementing the converter system 100 shown in FIG. 1. The computer 200includes at least one processor 202 coupled to a chipset 204. Thechipset 204 includes a memory controller hub 220 and an input/output(I/O) controller hub 222. A memory 206 and a graphics adapter 212 arecoupled to the memory controller hub 220, and a display 218 is coupledto the graphics adapter 212. A storage device 208, input interfaces 214,and network adapter 216 are coupled to the I/O controller hub 222. Otherembodiments of the computer 200 have different architectures.

The storage device 208 is a non-transitory computer-readable storagemedium such as a hard drive, compact disk read-only memory (CD-ROM),DVD, or a solid-state memory device. The memory 206 holds instructionsand data used by the processor 202. The input interfaces 214 may includea touch-screen interface, a mouse, track ball, or other type of pointingdevice, a keyboard, a scanner or other conventional digitizer, or somecombination thereof, and is used to input data, including images ofradiology film 101, into the computer 200. The graphics adapter 212displays images and other information on the display 218. The networkadapter 216 couples the computer 200 to one or more computer networks.

The computer 200 is adapted to execute computer program modules forproviding functionality described herein. As used herein, the term“module” refers to computer program logic used to provide the specifiedfunctionality. Thus, a module can be implemented in hardware, firmware,and/or software. In one embodiment, program modules are stored on thestorage device 208, loaded into the memory 206, and executed by theprocessor 202.

The type of computer 200 used for converter system 100 can varydepending upon the embodiment. For example, the converter system 100 mayinclude multiple computers 200 communicating with each other through anetwork to provide the functionality described herein. Such computers200 may lack some of the components described above, such as graphicsadapters 212 and displays 218.

Grid Block Segmentation

FIG. 3 is a block diagram illustrating an image segmentation module 120of a converter system 100 in accordance with an embodiment. The imagesegmentation module 120 identifies a grid pattern in the scanned imagein order to accurately segment it into grid block sub-images. In thisexample, the image segmentation module 120 includes a straight-linedetector 301, a sub-image separator 302, and sub-image storage 303.

The straight-line detector 301 of the image segmentation module 120 ofthe converter system 100 detects straight lines within the image. In oneembodiment, the straight-line detector 301 applies an edge-detectionpre-processing step according to any edge-detection methodology known tothose of skill in the art. An edge-detection pre-processing step may beused to identify or strengthen edges in the image by improving contrastbetween foreground and background before subsequent processing by thestraight-line detector 301. The straight-line detector 301 applies aHough Transform to the pre-processed image data to detect straight linesin the image. The straight-line detector 301 uses a statistical analysison the results of the Hough Transform that separately considers thelength of the detected lines at vertical and horizontal positions inorder to remove false-positive gridlines from the group of detectedstraight lines. The straight-line detector 301 clusters the remainingstraight lines together if they are within a short distance of eachother. The straight-line detector 301 identifies the mean position ofeach cluster of lines in the vertical direction as the center of avertical gridline, and the mean position of each cluster of lines in thehorizontal direction as the center of a horizontal gridline. Theseoperations will be described in more detail with reference to theflowcharts of FIGS. 4-5.

Referring again to FIG. 3, the sub-image separator 302 of the imagesegmentation module 120 uses the detected positions of the horizontaland vertical gridlines from the straight-line detector 301 to divide theimage accurately into sub-images. For example, each sub-image mayinclude an image from a single x-ray from among the patient's pluralityof x-rays collected on the radiology film 101.

The sub-image storage 303 at least temporarily stores the sub-imagesprocessed by the image segmentation module 120 for further processing bythe converter system 100. Because such sub-images may include sensitivepatient health information, adequate safeguards may be included invarious implementations of the converter system 100 to preventunauthorized access to the information and preserve patientconfidentiality.

FIG. 4 illustrates a method of performing grid block segmentation on animage of a radiology film in accordance with an embodiment. The methodis described as being performed by the image segmentation module 120 ofthe converter system 100, but other modules may perform steps of themethod. Further, in various implementations, different or additionalsteps may be performed to segment an image of radiology film 101 intosub-images.

In step 401, straight lines within the image of the radiology film 101are detected. Some of these straight lines are gridlines that divide animage into sub-images. Others of these lines are false-positivegridlines that may occur as the result of anatomical features, textcontent, or other features in the image that appear to be straight-lineswhen processed by the image segmentation module 120. In step 402,false-positive gridlines are removed from consideration as gridlinesbetween sub-images. Techniques for detecting 401 straight lines withinimages and removing 402 false-positive gridlines are discussed in moredetail below with reference to FIGS. 5-6. In step 403, the sub-imagesare separated based on the gridlines, and in step 404, the separatedsub-images are stored, for example in sub-image storage 303 for furtherprocessing by the converter system 100.

FIG. 5 is a flowchart illustrating a method of processing an image todetect straight lines and remove false-positive gridlines in accordancewith an embodiment. The method is described as being performed by theimage segmentation module 120 of the converter system 100, but othermodules may perform steps of the method. Further, in variousimplementations, different or additional steps may be performed todetect straight lines and remove false-positive gridlines.

In some implementations, in step 501, the image is pre-processed usingan edge-detection technique. Any edge-detection technique known to thoseof skill in the art may be used, such as Canny or Sobel operator, inorder to identify or strengthen edges in the image.

In step 502, a Hough Transform is applied. The Hough Transformidentifies lines by a voting procedure carried out in parameter space.However, the Hough Transform has several drawbacks when applied toradiology films. First, the images tend to have large intensityvariations which lead to the Hough Transform reporting many falsepositive lines. In addition, the presence of text in the image createsstrong edges that lead to confident Hough lines. If not remedied, thefalse positive lines stemming from intensity variations and text withinan image would lead to over-segmentation of the image into odd-shapedportions of images rather than discrete sub-images divided by gridlines.Second, the voting parameters of the Hough Transform are imagedependent, which leads to variations in the output. Third, the HoughTransform is subject to false negatives which would lead tounder-segmentation of the radiology film.

In step 503, the results of the Hough Transform are statisticallyanalyzed to reduce the false positives and false negatives discussedabove. A detailed example of the statistical analysis is described belowwith reference to FIG. 6.

In step 504, the image may be post-processed to prepare it forseparation into sub-images. Various conventional image post-processingtechniques may be used. In post-processing, the results obtained fromstatistical analysis can be further refined. For example, the distancebetween each line in the set of distinct gridlines obtained bystatistical analysis is calculated and the common repeating distance isvoted as the gridblock/sub-image size. This added information can thenbe used to cut the grid into sub-images which will now have the sameaspect ratio.

FIG. 6 is a flowchart illustrating an example method of identifying agrid pattern in an image of a radiology film by using a Hough Transformand statistically analyzing the results, in accordance with anembodiment of the invention. In some implementations, variations of theillustrated steps may be performed, some steps may be omitted, or thesteps may be performed in different orders.

In step 601, a Hough Line Transform is performed. As described above,the Hough Transform tends to result in false positives and falsenegatives. To reduce the instances of these false positives and falsenegatives, the method continues by finding the horizontal lines in step602 and the vertical lines in step 603, and then to sort the horizontaland vertical lines separately by distance from an origin, r, in step604. Next, the sorted lines are grouped by r and the lengths of the linesegments at each distance r_(i) are appended. This prepares the detectedline segments for finding a Simple Moving Average of lengths of thelines in step 606. In step 607, the mean and standard deviation aredetermined for the distribution of the Simple Moving Average, which inone embodiment is a normal distribution. In step 608, if the SimpleMoving Average at a particular point is less than or equal to threestandard deviations away from the mean, then the line is discarded as afalse positive in step 609. In step 610, if the Simple Moving Average ata particular point is more than three standard deviations away from themean, then the line is selected for clustering in step 610. In otherembodiments, different cutoffs, such as 2.5 standard deviations may beused to adjust the number of false positives that are discarded 609versus lines selected for clustering 610. The straight-line detector 301clusters the remaining straight lines together if they are within ashort distance of each other, for example if they are withinapproximately 1% of the image dimension. For example, for a 4000 pixelimage dimension, the segments within 40 pixels will be clusteredtogether. The straight-line detector 301 identifies the mean position ofeach cluster of lines in the vertical direction as the center of avertical gridline, and the mean position of each cluster of lines in thehorizontal direction as the center of a horizontal gridline. To identifymissing lines between grid blocks, the grid block size is estimatedusing a voting procedure. The distance is calculated between each linein the set of distinct gridlines obtained by statistical analysis, andthe common repeating distance is voted as the distance between thegridlines that makes up a single grid block. This repeating distance isused to extrapolate and add the missing lines between grid blocks.

FIG. 7A illustrates an example graph of the length of horizontalHoughLines at various distances from the origin. FIG. 7B illustrates anexample output of lines from the statistical analysis of the data inFIG. 7A according to the method illustrated in FIG. 6. As can be seen inFIG. 7B, strong lines in tight clusters are returned at relativelyevenly spaced distances from the origin, which correspond to horizontalgridlines between the sub-images.

FIG. 8A illustrates an example graph of the length of verticalHoughLines at various distances from the origin. FIG. 8B illustrates anexample output of lines from the statistical analysis of the data inFIG. 8A according to the method illustrated in FIG. 6. As can be seen inFIG. 8B, strong lines in tight clusters are returned at four relativelyevenly spaced distances from the origin, which correspond to verticalgridlines between the sub-images.

The positions of the gridlines shown in FIG. 7B and 8B are used by thesub-image separator 302 of the image segmentation module 120 to dividethe image accurately into sub-images. Thus, embodiments of the inventionavoid over-segmentation and under-segmentation of the image that arecommon problems with the bare application of the Hough Transform byusing a statistical analysis of the Hough transform results to identifygridlines between sub-images.

Text Processing

FIG. 9 is a block diagram illustrating a metadata extraction module of aconverter system in accordance with an embodiment. The metadataextraction module extracts text from the scanned radiology film 101 tobe used as DICOM metadata for a DICOM-compliant file. The metadataextraction module 130 includes a text region detection module 901, atext region processing module 902, a text recognition module 903, andtext storage 904.

The text region detection module 901 detects regions of the image thatcontain text. An example of a method that the text region detectionmodule 901 may apply to detect text regions is described below withreference to FIG. 10.

The text region processing module 902 processes the text regions toprepare them for successful text recognition. An example of a methodthat the text region processing module 902 may apply to process textregions is described below with reference to FIG. 11.

The text recognition module 903 performs optical character recognition.An example of a method that the text recognition module 903 may apply torecognize text is described below with reference to FIG. 13.

The text storage 904 stores the text recognized by the text recognitionmodule 903. The stored text is used as DICOM metadata for theDICOM-compliant file that includes the associated sub-image from whichthe text was extracted.

FIG. 10 is a flowchart illustrating a method of text region detection inaccordance with an embodiment. This method may be performed, forexample, by a text region detection module 901 of a metadata extractionmodule 130 of the converter system 100. In some implementations,variations of the illustrated steps may be performed, some steps may beomitted, or the steps may be performed in different orders. The methodpresented here is similar to the method proposed by Chen, Bourlard, andThiran in “Text Identification in Complex Backgrounds Using SVM,”Proceedings of the 2001 IEEE Computer Society Conference on ComputerVision and Pattern Recognition. Text regions can be detected in part bylooking for short edge mixture patterns within a region. The methodbegins with a sub-image 1001 to be analyzed. In steps 1002 and 1003, thehorizontal and vertical edges present in text regions are found by usinga Sobel operator along the X direction and using a Sobel operator alongthe Y direction. Then, in steps 1004 and 1005, morphological dilationalong the X direction and along the Y direction are performed to connectedges into clusters. In one embodiment, different dilation operators canbe used so that vertical edges are connected in the horizontal directionand horizontal edges are connected in the vertical direction. Thedilation operations may have a rectangular shape, such as 5×1 for thevertical operator and 3×6 for the horizontal operator. In step 1006, theresulting two images are combined using a binary AND operation becausetrue text regions will have horizontal and vertical edges within thesame region of the image. Additionally, in step 1007, a conventionalconnected component analysis is performed to identify the boundaries ofthe regions of the sub-image where text is present.

FIG. 11 is a flowchart illustrating a method of processing text regionsto prepare them for text recognition in accordance with an embodiment.This method may be performed, for example, by a text region processingmodule 902 of a metadata extraction module 130 of the converter system100. In some implementations, variations of the illustrated steps may beperformed, some steps may be omitted, or the steps may be performed indifferent orders. The method begins with sharpening 1101 the identifiedtext regions according to any standard technique known to those of skillin the art. Then, a power law transformation 1102, for example γ=4, isapplied to the image data of the sharpened text regions. Next, athresholding 1103 process is applied, for example using Minimum CrossEntropy (MinCE), as described by Li and Lee in “Minimum Cross EntropyThresholding,” Pattern Recognition, Vol. 26, No. 4, pp. 617-625, 1993.MinCE is a standard technique known to those of skill in the art thatminimizes the cross entropy between the threshold image and the originalimage. The selection of a threshold will affect both the accuracy andthe efficiency of the analysis of the segmented image. The bestthreshold loses the least information during the thresholding.Advantageously, no a priori assumptions are made about the populationdistribution during the MinCE thresholding process. After thethresholding 1103, hole filling 1104 is performed. Hole filing is amathematical operation that fills all gaps in the identified text regionaccording to standard techniques known to those of skill in the art.Lastly, in some implementations, bit manipulations 1105 are performed,for example an XOR operation is performed between one version of theimage of the text region that is thresholded, and another version of theimage of the text region that is thresholded and hole-filled. FIG. 12illustrates a comparison between text regions from the original image1201, text regions processed according to a conventional OtsuThresholding technique 1202, and text regions processed according totechniques in accordance with an embodiment of the invention, referredto as MinCE with hole filling 1203. The text regions processed accordingto MinCE with hole filling are substantially improved, with lessinterference from the complex background than the original image textregions 1201 or the Otsu Thresholding text regions 1202.

FIG. 13 is a flowchart illustrating a method of performing textrecognition in accordance with an embodiment. This method may beperformed, for example, by a text recognition module 903 of a metadataextraction module 130 of the converter system 100. The method begins instep 1301 by receiving a processed text region, for example from textregion processing module 902 of the metadata extraction module 130. Instep 1302, optical character recognition is performed on the processedtext region in order to recognize the text contained in the region. Anytechnique of optical character recognition known to those of skill inthe art may be used. In step 1303, the recognized text associated with arespective sub-image from which it was extracted is stored. Thus, therecognized text is available for use as DICOM metadata in aDICOM-compliant file including the analyzed sub-image.

In one embodiment, an evaluation tool can be used to evaluate the textextraction by using the Levenshtein Distance between the ground truth(the actual text) and the text recognized by the OCR process 1302. Forexample, the analysis can be performed using 1-e/c, where e is theLevenshtein distance, and c is the number of characters. Such anevaluation tool applied to the text extraction resulting from themethods described above shows that the methods described above performsubstantially better than OCR processes on text regions that have notbeen processed according to the methods described above.

DICOM File Generation

FIG. 14 is a flowchart illustrating a method of generating a DICOM filein accordance with an embodiment. This method may be performed, forexample, by a DICOM file generation module 140 of the converter system100. In some implementation, variations of the illustrated steps may beperformed. In step 1401, the segmented sub-images and associated textare accessed, for example from sub-image storage 303 of the imagesegmentation module 120 and from text storage 904 of the metadataextraction module 130. In step 1402, a DICOM-compliant multi-image fileis formatted according to the DICOM standard, including the associatedtext as DICOM metadata. In step 1403, the generated DICOM file can beoutput, for example to a different computer system for analysis,treatment, or research purposes.

Advantageously, the described converter system 100 can be integrated ontop of the image acquisition software of medical scanners. Thus, theimages from the medical scanners conveniently can be converted throughthe converter system 100 into a DICOM-compliant file 141 without delay.Alternatively, the described converter system 100 can be used as aresearch tool to remove all patient related information by redactingrecognized text that contains confidential patent information from theDICOM file before it is shared with researchers. Thus, the images canthen be used for research purposes appropriate to the type of image(e.g., classification of tissue, detection of tumor/fracture, etc.),without the risk of exposing sensitive information in violation ofprivacy laws.

Additional Configuration Considerations

Some portions of the above description describe the embodiments in termsof algorithmic processes or operations. These algorithmic descriptionsand representations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs comprising instructions for executionby a processor or equivalent electrical circuits, microcode, or thelike. Furthermore, it has also proven convenient at times, to refer tothese arrangements of functional operations as modules, without loss ofgenerality. The described operations and their associated modules may beembodied in software, firmware, hardware, or any combinations thereof.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the disclosure. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs. Thus,while particular embodiments and applications have been illustrated anddescribed, it is to be understood that the described subject matter isnot limited to the precise construction and components disclosed hereinand that various modifications, changes and variations which will beapparent to those skilled in the art may be made in the arrangement,operation and details of the method and apparatus disclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:detecting lines within an image of a scanned radiology film, the imageincluding a plurality of sub-images in a grid pattern; identifyinggridlines between sub-images based on a clustering of the detectedlines; separating sub-images based on the gridlines; and storing theseparated sub-images.
 2. The method of claim 1, wherein identifying thegridlines between sub-images based on the clustering of the linescomprises: clustering the detected lines within a short distance of eachother; and identifying a mean position of each cluster as a position ofa gridline.
 3. The method of claim 1, wherein identifying the gridlinesbetween sub-images based on the clustering of the lines comprises:determining a common repeating distance between gridlines identified bythe clustering; and identifying missing gridlines by extrapolatingaccording to the common repeating distance from the gridlines identifiedby the clustering.
 4. The method of claim 1, wherein identifying thegridlines between the sub-images comprises: discarding false positivesfrom the detected lines based on a statistical analysis of lengths ofthe detected lines; and identifying gridlines between sub-images basedon a clustering of a subset of the detected lines remaining afterdiscarding the false positives.
 5. The method of claim 4, whereindiscarding the false positives from the detected lines comprises:finding a Simple Moving Average of the lengths of the detected linessorted by distance from an origin; determining a mean and standarddeviation of a distribution of the Simple Moving Average; and discardinga line as a false positive responsive to the Simple Moving Averagecorresponding to the line being less than a cutoff number of standarddeviations from the mean.
 6. The method of claim 1, further comprisingdetecting text regions within the sub-images by: detecting horizontaland vertical edges of the text regions in horizontal and verticaldirections; connecting the detected horizontal and vertical edges intoclusters by performing morphological dilation along the horizontaldirection and along the vertical direction; and performing a binary ANDoperation to identify text regions that have horizontal and verticaledges within a same region of a respective sub-image.
 7. The method ofclaim 1, further comprising: detecting text regions within thesub-images; performing optical character recognition to recognize textfrom a respective text region of a respective sub-image; and storing thetext associated with the respective sub-image.
 8. The method of claim 7,further comprising: accessing the separated sub-images and textassociated with the respect sub-images; formatting a DICOM-compliantmulti-image file based on the sub-images and associated text; andoutputting the DICOM-compliant file.
 9. A non-transitorycomputer-readable storage medium storing executable computer programinstructions, the instructions executable to perform steps comprising:detecting lines within an image of a scanned radiology film, the imageincluding a plurality of sub-images in a grid pattern; identifyinggridlines between sub-images based on a clustering of the detectedlines; separating sub-images based on the gridlines; and storing theseparated sub-images.
 10. The computer-readable medium of claim 9,wherein identifying the gridlines between sub-images based on theclustering of the lines comprises: clustering the detected lines withina short distance of each other; and identifying a mean position of eachcluster as a position of a gridline.
 11. The computer-readable medium ofclaim 9, wherein identifying the gridlines between sub-images based onthe clustering of the lines comprises: determining a common repeatingdistance between gridlines identified by the clustering; and identifyingmissing gridlines by extrapolating according to the common repeatingdistance from the gridlines identified by the clustering.
 12. Thecomputer-readable medium of claim 9, wherein identifying the gridlinesbetween the sub-images comprises: discarding false positives from thedetected lines based on a statistical analysis of lengths of thedetected lines; and identifying gridlines between sub-images based on aclustering of a subset of the detected lines remaining after discardingthe false positives.
 13. The computer-readable medium of claim 12,wherein discarding the false positives from the detected linescomprises: finding a Simple Moving Average of the lengths of thedetected lines sorted by distance from an origin; determining a mean andstandard deviation of a distribution of the Simple Moving Average; anddiscarding a line as a false positive responsive to the Simple MovingAverage corresponding to the line being less than a cutoff number ofstandard deviations from the mean.
 14. The computer-readable medium ofclaim 9, further comprising detecting text regions within the sub-imagesby: detecting horizontal and vertical edges of the text regions inhorizontal and vertical directions; connecting the detected horizontaland vertical edges into clusters by performing morphological dilationalong the horizontal direction and along the vertical direction; andperforming a binary AND operation to identify text regions that havehorizontal and vertical edges within a same region of a respectivesub-image.
 15. The computer-readable medium of claim 9, wherein thecomputer-readable medium further comprises computer program instructionsfor: detecting text regions within the sub-images; performing opticalcharacter recognition to recognize text from a respective text region ofa respective sub-image; and storing the text associated with therespective sub-image.
 16. The computer-readable medium of claim 15,wherein the computer-readable medium further comprises computer programinstructions for: accessing the separated sub-images and text associatedwith the respect sub-images; formatting a DICOM-compliant multi-imagefile based on the sub-images and associated text; and outputting theDICOM-compliant file.
 17. A system comprising: a processor; and anon-transitory, computer-readable medium comprising executable computerprogram instructions, the instructions executable by the processor toperform steps comprising: detecting lines within an image of a scannedradiology film, the image including a plurality of sub-images in a gridpattern; identifying gridlines between sub-images based on a clusteringof the detected lines; separating sub-images based on the gridlines; andstoring the separated sub-images.
 18. The system of claim 17, whereinidentifying the gridlines between sub-images based on the clustering ofthe lines comprises: clustering the detected lines within a shortdistance of each other; and identifying a mean position of each clusteras a position of a gridline.
 19. The system of claim 17, whereinidentifying the gridlines between sub-images based on the clustering ofthe lines comprises: determining a common repeating distance betweengridlines identified by the clustering; and identifying missinggridlines by extrapolating according to the common repeating distancefrom the gridlines identified by the clustering.
 20. The system of claim17, wherein the computer-readable medium further comprises computerprogram instructions for: detecting text regions within the sub-images;performing optical character recognition to recognize text from arespective text region of a respective sub-image; formatting aDICOM-compliant multi-image file based on the sub-images and associatedtext; and outputting the DICOM-compliant file.